The present invention relates to the measurement and analysis of bioelectro-magnetic activity in electrically active organs. More specifically, the present invention relates to a method of transforming the measurements into a corresponding current distribution map estimating the original sources and combining current distribution maps of two or more states of organ activity into difference maps that reveal regions of the organ in which activity differs for the different states.
The ion currents of electrically active organs such as the brain and heart can generate magnetic fields that can be measured outside the surface of the body. Further, the corresponding electrical potentials in the organs themselves, when conducted through the body, can be measured on the skin, using surface electrodes or in the interior of the body by means of invasive depth electrodes. The process of computing the biological source current or currents giving rise to the observed magnetic and electrical measurements is generally referred to as xe2x80x9cthe bioelectromagnetic inverse problemxe2x80x9d. The importance of having a biomagnetic or bioelectric inverse solution is that it can be used to correlate electrophysiological function with a particular coordinate within the body. This, in turn, can be used to associate normal function and dysfunction with specific anatomic structures. It can be shown that, in three dimensions, there can be no unique bioelectromagnetic inverse solution without applying constraints to the solution, such as assuming the number and configuration of possible sources. Notwithstanding this, it is possible to calculate useful estimates or approximations of the distribution and intensity of source activity from electrophysiological measurements.
In the present art, Magnetoencephalographic (MEG) and Electroencephalographic (EEG) signals may be examined for waveform morphology in independent channels, characterized, for example, by their frequency and amplitude. In addition, MEG and/or EEG measurements, recorded from a plurality of sites, are often represented as topographic distributions of either spontaneous or evoked signals in the form of signal intensity maps about the head. Such topographic maps are also commonly presented for MEG/EEG in distinct frequency bands.
It is also known to average MEG and EEG signals synchronously with a stimulus presented to the subject or to a voluntary motor movement from the subject. Signal averaging can improve the signal-to-noise ratio (SNR) of the brain activity underlying a particular sensory or motor event. The resulting averaged signal is conventionally known as the event-related potential (ERP) or the event-related field (ERF). The averaged evoked response is most useful for improving the SNR or activity in the primary cerebral cortex, in which the time delay between stimulus and response has low variability. However, the evoked response relating to higher cognitive functions, which involve associative cerebral cortex, can be more variable in time delay and duration relative to the driving stimulus. Thus, signal averaging is less useful for evaluating higher cognitive functions. The application of signal averaging to EEG and MEG brain signals is predicated upon the notion that the underlying neural events are identical with each and every stimulus event. Common sense and personal experience dictate that this is not necessarily the case for higher levels of brain functioning. The delay between external events and related thought processes is known to vary greatly. Brain activity associated with critical higher mental processes, such as the production and understanding of language, are therefore not adequately represented by the averaged evoked response.
Another known representation of the MEG and EEG signals is the Equivalent Current Dipole (ECD). The ECD can be computed by fitting a simplified model of a current dipole (or multiple dipoles), each characterized by a location, current vector, and magnitude, to the MEG and/or EEG measurements at some selected instant of time, usually in the least squares sense. In the Minimum Norm Current Distribution method, a more complex, often under-determined model is fitted to the measurements at some instant of time by a least squares method. Both the ECD and Minimum Norm methods can yield erroneous results (e.g., inaccurate localization and magnitude of cortical generators) when noise is present in the EEG or MEG signal. When the Minimum Norm solution is underdetermined (and it almost always is), the non-uniqueness of the inverse problem implies that the result is only one of many possible source configurations that can explain the measurements. Thus, only spontaneous EEG or MEG signals having high signal-to-noise ratio and a source characterized by few parameters, such as epileptic spikes and abnormal high-amplitude xe2x80x9cslow wavesxe2x80x9d (a sign of closed-head injury) can be localized accurately by these two methods. Normal (non-pathological) events within the brain are of much lower amplitude; when possible, signal averaging is conventionally used to improve the signal-to-noise ratio of such events.
Much of the above-mentioned prior art is described in SQUID-Based Measuring Techniques by Manfried Hoke in THE ART OF MEASUREMENT METROLOGY IN FUNDAMENTAL AND APPLIED PHYSICS, edited by B. Kramer (1988).
The activity of electrically active organs, such as the brain, may also be monitored and imaged using Positron Emission Tomography (PET) and fictional magnetic resonance imaging (fMRI). Neither of these imaging modalities are direct measures of the electrochemical events that comprise neural activity. Instead, they detect local changes in metabolism, metabolic products or blood flow within the brain. These changes are consequent to the energy requirements of the electrochemical events. Although electrochemical events can occur in less than one millisecond, corresponding local changes in metabolism and blood flow are much slower, having time constants of several seconds. Hence, PET and fMRI lack the time resolution of EEG and MEG, as they are indirect measures of brain activity.
Lead Field Synthesis (LFS) departs from previous methods for analyzing bioelectromagnetic measurements. LFS is disclosed by S. E. Robinson and W. C. Black in U.S. Pat. No. 4,977,896 (Robinson et al.) and U.S. Pat. No. 5,269,325 (Robinson et al.) entitled xe2x80x9cAnalysis of Biological Signals using Data from Arrays of Sensorsxe2x80x9d. Instead of localizing brain activity, LFS increases the spatial selectivity of an array of MEG sensors by summing the weighted observations. The weights are selected to impart higher spatial selectivity to a specified coordinate in the head. The sum of products of the measured signal and these weights results in a xe2x80x9cvirtual sensorxe2x80x9d that estimates electrical activity as a function of time at the selected location.
It is also known that the bioelectromagnetic inverse solution can be improved by constraining the location of source currents to the cortex of the brain, since it is the electrical currents flowing between the dendrites and cell bodies of the neurons that are the primary contributors to the measured magnetic fields and electrical potentials. Furthermore, the source current is known to flow in a direction approximately normal to each point on the cortical surface which provides an additional constraint for the inverse solution. The coordinates and vectors describing the cortical surface can be extracted from anatomical images of the brain. These images can be obtained, for example, using magnetic resonance imaging (MRI) or computed tomography (CT) scanning of the head.
While certain advances have been made in this art, there is still much room for improvement. For example, heretofore, the prior art approach has been unable to localize brain activity in a manner which can adequately represent spontaneous (unaveraged) activity (e.g., brain activity), particularly that of normal higher cognitive functions.
Specifically, certain prior art approaches (e.g., the ECD and Minimum Norm methods discussed above) rely on xe2x80x9cmodel-fittingxe2x80x9d techniques generally involving the following steps:
(i) the signals are initially observed at some instant or time sample;
(ii) a parametric model is used to predict a forward solution for the measurements; and
(iii) the parameters of the model are adjusted so as to simultaneously minimize the differences between measured and predicted signals at each of the sensorsxe2x80x94usually in the least-squares sense.
As an example, a single ECD may be described using five free parameters for magnetic measurementsxe2x80x94three for position, one describing the tangential dipole-moment vector (radial currents are xe2x80x9csilentxe2x80x9d in magnetic measurement) and one describing the dipole-moment magnitude. Further, for ECD representation to be operable, a relatively high signal-to-noise (SNR) ratio is needed. However, for certain organs such as the brain, the spontaneous signals generated by small functional regions do not provide an adequate SNR ratio (in the case of the brain, this is due to the fact that brain functions are also being carried out in areas which are not of interest). Accordingly, it is necessary to use signal averaging techniques to improve the SNR ratio. For signal averaging techniques to be useful, it is necessary that the regions of interest in, for example, the brain are in precise synchrony with external events resulting in poor vision of associative areas. Accordingly, practical imaging of high brain function from MEG and EEG signals has heretofore been at least difficult (if not possible) to achieve. In the Minimum Norm solutions, there are many times more free parameters. When there are more parameters than sensors (measurements) the problem becomes underdetermined.
Certain other prior art approaches (e.g., the LFS method discussed above) rely generally involve the following steps
(i) the signals are initially observed;
(ii) observed signals are weighted by some coefficient; and
(iii) derivation of an additional signal which is an estimate of activity (e.g., brain activity).
This approach has limited value in evaluating higher cognitive functions due to the rapid fluctuations (liability) of the activity of certain organs (e.g., the brain).
It would be desirable to have a system and method for measuring, estimating and displaying root mean square (RMS) current density maps which obviates or mitigates the above-mentioned limitations of the prior art.
It is an object of the present invention to provide a novel apparatus and method for measuring, estimating, and displaying RMS current density maps of brain activity which obviates or mitigates at least one of the disadvantages of the prior art methods for calculating estimates of the distribution and intensity of source activity from electrophysiological measurements.
The system and method of the present invention, referred to herein as Synthetic Aperture Magnetometery (SAM) methodology, permits tomographic imaging of brain activity and represents a radical departure from the previously described prior art methods for analysing MEG and/or EEG data. In contrast to the conventional methods (e.g., ECD, minimum norm and LFS taught in Robinson et al. patents), SAM converts the measured data from, for example when brain activity is being evaluated, an MEG and/or EEG sensor array over a segment of time (rather than at a single instant), into an estimate of the RMS source current density at any designated location in the head. The present invention also provides, in the example of evaluating brain activity, a method for displaying the brain activity that differs between two or more states of brain activity. This latter process is referred to herein as Differential Current Density Mapping (CDM). In applying DCDM, individual SAM images are derived from MEG and/or EEG data which has been partitioned into discrete time segments. The time segments correspond to at least two mental states under examination. The SAM image derived for each mental state are then combined using DCDM to display the locations and intensities of the brain that differ between the at least two mental states, Since the common mode brain activity is attenuated by the subtraction process, the locations and interactions of the brain activity that differ between any two brain states is readily identified.
Accordingly, in one aspect of the present invention, there is provided a method of performing synthetic aperture magnetometery on the signals from a target organ using an array of biomagnetic sensors positioned in a predetermined manner around the target organ (e.g., the brain), each sensor in the array having a position vector and an orientation vector relative to a common coordinate system encompassing the target organ (e.g., the brain), the method comprising the steps of:
(i) simultaneously measuring EM signals from each sensor positioned in the array for a selected time interval;
(ii) computing a covariance matrix of the measured EM signals over a user-selected time sub-interval within the selected time interval;
(iii) selecting a set of coordinates for a region of interest to be imaged and a distance between voxels to form a grid of voxels;
(iv) computing a forward solution for a current element at each of the voxels for each of the sensors in the array sensors;
(v) computing an RMS current density estimate for each voxel from the covariance matrix and the forward solution for that voxel; and
(vi) displaying the voxels estimating RMS current density as a first image. This method is typical of SAM.
Thus, in the present method, as will be illustrated below, when reference is made to positioning of biomagnetic sensors in a predetermined manner around a target organ, those of skill in the art will appreciate that this means that the sensors are placed in the vicinity of the target organ, usually external to the surface of the body. As is known in the art, the sensitivity of biomagnetic sensors declines rapidly with distance (inverse cube law for simple magnetometers, inverse 4th power for first-order gradiometers, etc.). Also, the fine (i.e., xe2x80x9chigher-orderxe2x80x9d) spatial features of the biomagnetic field, necessary for distinguishing different sources, also decline with distance. This means that the biomagnetic sensors must be placed as close to the body as is practically feasible. Ideally, biomagetic sensors of a number sufficient to obtain as many different xe2x80x9cperspectivexe2x80x9d measurements as is possible are placed around or in the vicinity of the target organ. Further, the common coordinate system encompassing the target organ is related to the position vector and orientation vector of each sensor in the array. The magnetic field of the target organ (e.g., the brain, as well as other organs), is a vector quantity. The features of such a field convey information as to the location and intensity of each of the cortical generators (sources). The field should be sampled, spatially, at small enough intervals, surrounding as much of the target organ as possible, to convey information needed for localization and imaging.
As used throughout this specification, the term xe2x80x9cEM signalsxe2x80x9d is intended to mean the signals generated from ion currents in electrically active organs. Generally, these signals will be bioelectric signals, biomagnetic signals or a combination of these. Thus, if the electrically active organ is a brain the EM signals can be magnetoencephalogram (MEG) signals, electroencephalogram (BEG) signals or a combination of these. Alternatively, if the electrically active organ is a heart the EM signals can be electrocardiogram (ECG) signals, magnetocardiogram (MCG) signals or a combination of these. Further alternatively, if the electrically active organ is an eye, the EMG signals can be electrooculogram (EOG) signals, magnetooculogram (MOG) signals or a combination of these. Persons of skill in the art will recognize that the precise nomenclature of the EM signals useful in the present method will depend on the particular target organ.
Further, as used throughout this specification, the term xe2x80x9cforward solutionxe2x80x9d is intended to mean a computation of the magnetic field or electrical potential response of a mathematically modelled sensor or electrode to a mathematically modeled current distribution within a mathematically modeled conducting volume. As will be understood by those of skill in the art, closed mathematical solutions exist for a unique forward solution of signal from source. By contrast, there is no closed and unique mathematical solution for the bioelectromagnetic inverse. Hence, all inverse solutions rely upon forward solutions. More information on forward solutions may be found in Basic Mathematical and Electro magnetic Concepts of the Biomagnetic Inverse Problem by J. Sarvas (Phys. Med. Biol. 32:11-22 (1987)).
Preferably, the method includes repeating Steps (ii) through (vi) over a second time sub-interval within the selected time interval, to produce a second RMS current density image and, the additional step of subtracting the second RMS current density image, voxel by voxel, from the first image to form a third RMS current density image representing the difference source activity in the brain between the first and second time windows. This preferred embodiment is typical of DCDM.